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An Operational Approach to Multi-Objective Optimization for Volt-VAr Control

Author

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  • David Raz

    (HIT—Holon Institute of Technology, Holon 5810201, Israel
    These authors contributed equally to this work.)

  • Yuval Beck

    (School of Electrical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
    These authors contributed equally to this work.)

Abstract

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder.

Suggested Citation

  • David Raz & Yuval Beck, 2020. "An Operational Approach to Multi-Objective Optimization for Volt-VAr Control," Energies, MDPI, vol. 13(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5871-:d:442879
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    References listed on IDEAS

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    1. José Raúl Castro & Maarouf Saad & Serge Lefebvre & Dalal Asber & Laurent Lenoir, 2016. "Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic," Energies, MDPI, vol. 9(2), pages 1-16, February.
    2. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    3. Zare, Mohsen & Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Amiri, Babak, 2014. "Multi-objective probabilistic reactive power and voltage control with wind site correlations," Energy, Elsevier, vol. 66(C), pages 810-822.
    4. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    5. Arthur M. Geoffrion, 1967. "Solving Bicriterion Mathematical Programs," Operations Research, INFORMS, vol. 15(1), pages 39-54, February.
    6. Jones, D. F. & Mirrazavi, S. K. & Tamiz, M., 2002. "Multi-objective meta-heuristics: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 137(1), pages 1-9, February.
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    Cited by:

    1. Gaurav Yadav & Yuan Liao & Nicholas Jewell & Dan M. Ionel, 2022. "CVR Study and Active Power Loss Estimation Based on Analytical and ANN Method," Energies, MDPI, vol. 15(13), pages 1-19, June.

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